AI agents and RAG workflows can pull from multiple data sources, combine results, and respond in ways that are hard to predict at design time. That makes static permissions too blunt for many use cases. Context-aware controls help decide whether a user should receive specific information at that moment, based on role, business purpose, and the sensitivity of the data involved.
Why This Matters for Security Teams
AI agents and RAG workflows do not behave like static applications. They assemble answers from changing context, chain tool calls, and can surface data that was never intended for the original request. That is why role-only access control is often too coarse: the same user may be entitled to one dataset in one context but not when the agent is operating across multiple repositories, vendors, or business functions.
This risk is not theoretical. NHIMG’s AI Agents: The New Attack Surface report notes that 80% of organisations say their agents have already acted beyond intended scope, including unauthorised access, sensitive data sharing, and credential exposure. That finding aligns with the direction of the NIST AI Risk Management Framework and the OWASP Agentic AI Top 10, both of which emphasise runtime governance over assumptions made at design time.
In practice, many security teams encounter overexposure only after an agent has already combined allowed data into an unintended disclosure path.
How It Works in Practice
Context-aware access decisions evaluate the request at runtime, not just the identity of the caller. For AI agents and RAG systems, that means the policy engine considers what the agent is trying to do, which data it is attempting to retrieve, the sensitivity of the source, the user’s purpose, the current session, and whether the action is consistent with the approved workflow. This is the practical response to a problem highlighted in NHIMG’s OWASP NHI Top 10 coverage: agents do not follow a fixed path, so pre-defined access assumptions age quickly.
Common implementation patterns include:
- Policy-as-code checks at each retrieval or tool invocation.
- Short-lived, task-bound credentials instead of broad standing access.
- Document- and field-level filtering so the model only sees approved context.
- Workload identity for the agent, so the system can verify what the agent is rather than relying only on a user session.
- Audit logs that capture the prompt, retrieval set, and policy decision together.
For agents that use external tools, the security boundary must include the tool chain itself. A retrieval step may be harmless, while the next action may write to a ticketing system, send email, or query a finance dataset. That is why many teams are moving toward runtime policy evaluation using controls described in the CSA MAESTRO agentic AI threat modeling framework and implementing identity proof with standards such as SPIFFE where the environment supports it. These controls tend to break down when legacy applications expose coarse API permissions because the policy engine cannot distinguish safe retrieval from unsafe downstream reuse.
Common Variations and Edge Cases
Tighter context-aware controls often increase latency, policy complexity, and review overhead, so organisations must balance precision against operational speed. The tradeoff is especially visible in high-volume RAG systems where every query may touch multiple sources and every source may have different sensitivity labels.
There is no universal standard for this yet. Current guidance suggests using stricter context checks for high-risk actions, but allowing lighter controls for low-risk summarisation or draft generation. That means a customer-facing chatbot, an internal knowledge assistant, and a code-writing agent may each need different policy depth even if they share the same model backend.
Edge cases usually appear where data classification is incomplete or where the agent can chain benign actions into a harmful sequence. NHIMG’s The State of Secrets in AppSec research is relevant here because secrets and tokens often become the hidden bridge between retrieval and execution. In those environments, context-aware access should be paired with secret minimisation and automatic revocation, not treated as a standalone fix. Security leaders should also watch for indirect prompt injection and cross-domain retrieval paths, which the NIST AI Risk Management Framework and the OWASP Non-Human Identity Top 10 both treat as governance and identity problems, not just model problems.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10, CSA MAESTRO and OWASP Non-Human Identity Top 10 address the attack and risk surface, while NIST AI RMF and NIST Zero Trust (SP 800-207) set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | A03 | Agentic systems need runtime controls because behavior is dynamic and hard to predict. |
| CSA MAESTRO | TRM | MAESTRO maps agent workflows to threat paths and control points. |
| NIST AI RMF | AI RMF governs lifecycle risk, accountability, and runtime monitoring for AI systems. | |
| OWASP Non-Human Identity Top 10 | NHI-04 | Non-human identities need least privilege and short-lived access to limit blast radius. |
| NIST Zero Trust (SP 800-207) | 3.1 | Zero Trust requires continuous verification and context-aware authorization. |
Enforce request-time policy checks for every agent action, retrieval, and tool invocation.
